Hans Skov-Petersen

Recreational site choice modelling using high-resolution spatial data

Geographic information systems (GIS) offer attractive tools for modelling recreational behaviour. In this paper GIS is combined with a discrete choice modelling approach to investigate the importance of landscape attributes for determining forest recreational choices. A new large Danish national dataset (28 947 recreational trips) is used in conjunction with an area information system and information on other site attributes to estimate a recreational choice model. A wide set of landscape characteristics influencing recreational choice is identified. These are the size of the forest patch and adjacency or distance to other land-cover types. The area of the forest patch was found to have a positive marginally declining effect. Adjacency to other seminatural areas was found to have a positive effect. The results show that the parameter estimates depend critically on the choice-set specification and that the size of the choice set needs to be considerably larger than previous research suggests.

Urban planning practices for bikeable cities – the case of Copenhagen

Cities are increasingly looking to cycling as a means to promote sustainability, liveability, and public health. Denmark is one of the European countries where cycling has remained significant – even in the motor age, and national strategies now aim to support and further increase cycling. The article presents the state of cycling in Denmark and Copenhagen – as well as the cycling promoting efforts of the city of Copenhagen which is one of the forerunners in the field.

Have a nice trip: an algorithm for identifying excess routes under satisfaction constraints

Analysis of potential spatial behavior in transport infrastructures is usually carried out by means of a digital network. A basic condition for such a network analysis has traditionally been the desire to find solutions to optimization problems and to achieve greater efficiency in industry. Geographic information system (GIS) tools for network analysis are overwhelmingly targeted at finding solutions to optimization problems, which include the shortest path problem and the traveling salesman problem. This article addresses the problem of the lack of tools for finding solutions to a class of constraint satisfaction problems that are of potential interest to behavioral geographers. Constraint satisfaction problems differ from optimization problems in that they lack an expression to be maximized or minimized. We describe how a constraint-based approach to network analysis can be applied to search for ‘excess routes’ that are longer or in other ways exceed single, optimal routes. Our analysis considers both round-trips and travel from A to B and defines a set of constraints that can characterize such paths. We present a labeling algorithm that can generate solutions to such excess route problems.

How do cyclists make their way? - A GPS-based revealed preference study in Copenhagen

ABSTRACT It is the objective of the study to determine the extent to which human navigation is affected by perceptions of our immediate surroundings or by already established knowledge in terms of a cognitive map. The motivation is to contribute to the knowledge about human navigation and to inform planning with estimates of bicyclists’ route preferences and ‘willingness-to-pay’ (in terms of transport distance vs. utility/disutility of route characteristics). The core method is choice modelling of observed route data. Thousand two hundred and sixty seven trips performed by 183 cyclists in Copenhagen (Denmark) were recorded by GPS. The trips were map-matched to a digital road and path network, which enabled the generation of choice sets: one for navigation as influenced by perception of immediate surroundings, comprising edges connected to network-nodes (hereafter called the edge dataset), and one for navigation based on a priori knowledge, comprising the trip itself and a number of alternative routes generated by a labelling algorithm (hereafter called the route dataset). The results document that choices based on characteristics of both the route and the edge data can be estimated and provide reasonable and significant parameter estimates regarding cyclists’ preferences. Length was significant and negative, illustrating that cyclists – everything else kept equal – prefer to bike shorter distances. Preferences regarding characteristics of bike path, presence of traffic lights and road types show similar results to the two types of data; most importantly that routes with facilities, such as curbed tracks and segregated bikeways, were significantly preferred. The study concludes that cyclists’ wayfinding can be modelled as choices based on both an edge dataset and a route dataset and, thus, may be influenced by both perceived information and a priori knowledge. We suggest that future analyses of movement and route preferences take both modes into account as actual movement may be based on a combination of the two and because assessment of the influence of the immediate, perceivable surroundings can provide information not to be considered in a wayfinding approach. In our case differences in preferences along the route is for example found, but this can be expanded in future studies to also include dynamic aspects such as weather and crowding.